Overview

Dataset statistics

Number of variables22
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory747.1 KiB
Average record size in memory382.5 B

Variable types

Numeric15
Categorical7

Variable descriptions

idID
battery_powerTotal energy a battery can store in one time measured in mAh
blueHas bluetooth or not
clock_speedspeed at which microprocessor executes instructions
dual_simHas dual sim support or not
fcFront Camera mega pixels
four_gHas 4G or not. 1 = yes , 0 = no
int_memoryinternal Memory in Gigabytes
m_depMobile Depth in cm
mobile_wtWeight of mobile phone
n_coresNumber of cores of processor
pcPrimary Camera mega pixels
px_heightPixel Resolution Height
px_widthPixel Resolution Width
ramRandom Access Memory in Mega Bytes
sc_hScreen Height of mobile in cm
sc_wScreen Width of mobile in cm
talk_timelongest time that a single battery charge will last when you are
three_gHas 3G or not
touch_screenHas touch screen or not, 1 = yes, 0 = no
wifiHas wifi or not
price_categoryThis is the target variable with indicating if the mobile phone got a high price. 1 = yes, 0 = no

Warnings

id is uniformly distributed Uniform
id has unique values Unique
fc has 474 (23.7%) zeros Zeros
pc has 101 (5.1%) zeros Zeros
sc_w has 180 (9.0%) zeros Zeros

Reproduction

Analysis started2021-05-02 04:19:54.379531
Analysis finished2021-05-02 04:20:35.721998
Duration41.34 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

ID

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.5
Minimum0
Maximum1999
Zeros1
Zeros (%)< 0.1%
Memory size15.8 KiB
2021-05-02T12:20:35.827901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99.95
Q1499.75
median999.5
Q31499.25
95-th percentile1899.05
Maximum1999
Range1999
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation577.4945887
Coefficient of variation (CV)0.5777834805
Kurtosis-1.2
Mean999.5
Median Absolute Deviation (MAD)500
Skewness0
Sum1999000
Variance333500
MonotocityStrictly increasing
2021-05-02T12:20:35.964597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19991
 
0.1%
13401
 
0.1%
13141
 
0.1%
13161
 
0.1%
13181
 
0.1%
13201
 
0.1%
13221
 
0.1%
13241
 
0.1%
13261
 
0.1%
13281
 
0.1%
Other values (1990)1990
99.5%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
19991
0.1%
19981
0.1%
19971
0.1%
19961
0.1%
19951
0.1%
19941
0.1%
19931
0.1%
19921
0.1%
19911
0.1%
19901
0.1%

battery_power
Real number (ℝ≥0)

Total energy a battery can store in one time measured in mAh

Distinct1094
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5185
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:36.126048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.95
Q1851.75
median1226
Q31615.25
95-th percentile1930.15
Maximum1998
Range1497
Interquartile range (IQR)763.5

Descriptive statistics

Standard deviation439.4182061
Coefficient of variation (CV)0.3547934133
Kurtosis-1.224143883
Mean1238.5185
Median Absolute Deviation (MAD)382
Skewness0.03189847179
Sum2477037
Variance193088.3598
MonotocityNot monotonic
2021-05-02T12:20:36.262364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15896
 
0.3%
6186
 
0.3%
18726
 
0.3%
13795
 
0.2%
13105
 
0.2%
10635
 
0.2%
8325
 
0.2%
14145
 
0.2%
14135
 
0.2%
18075
 
0.2%
Other values (1084)1947
97.4%
ValueCountFrequency (%)
5012
 
0.1%
5022
 
0.1%
5033
0.1%
5045
0.2%
5061
 
0.1%
5072
 
0.1%
5083
0.1%
5091
 
0.1%
5103
0.1%
5114
0.2%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19962
0.1%
19952
0.1%
19943
0.1%
19931
 
0.1%
19922
0.1%
19914
0.2%
19892
0.1%
19881
 
0.1%

blue
Categorical

Has bluetooth or not

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size116.4 KiB
no
654 
yes
623 
Yes
173 
No
165 
NO
96 
Other values (5)
289 

Length

Max length3
Median length3
Mean length2.5425
Min length2

Characters and Unicode

Total characters5085
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowhas
5th rowyes
ValueCountFrequency (%)
no654
32.7%
yes623
31.1%
Yes173
 
8.6%
No165
 
8.2%
NO96
 
4.8%
YES79
 
4.0%
has62
 
3.1%
Has53
 
2.6%
Not48
 
2.4%
not47
 
2.4%
2021-05-02T12:20:36.531508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:36.619767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
no915
45.8%
yes875
43.8%
has115
 
5.8%
not95
 
4.8%

Most occurring characters

ValueCountFrequency (%)
o914
18.0%
s911
17.9%
e796
15.7%
n701
13.8%
y623
12.3%
N309
 
6.1%
Y252
 
5.0%
a115
 
2.3%
O96
 
1.9%
t95
 
1.9%
Other values (4)273
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4217
82.9%
Uppercase Letter868
 
17.1%

Most frequent character per category

ValueCountFrequency (%)
o914
21.7%
s911
21.6%
e796
18.9%
n701
16.6%
y623
14.8%
a115
 
2.7%
t95
 
2.3%
h62
 
1.5%
ValueCountFrequency (%)
N309
35.6%
Y252
29.0%
O96
 
11.1%
E79
 
9.1%
S79
 
9.1%
H53
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin5085
100.0%

Most frequent character per script

ValueCountFrequency (%)
o914
18.0%
s911
17.9%
e796
15.7%
n701
13.8%
y623
12.3%
N309
 
6.1%
Y252
 
5.0%
a115
 
2.3%
O96
 
1.9%
t95
 
1.9%
Other values (4)273
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5085
100.0%

Most frequent character per block

ValueCountFrequency (%)
o914
18.0%
s911
17.9%
e796
15.7%
n701
13.8%
y623
12.3%
N309
 
6.1%
Y252
 
5.0%
a115
 
2.3%
O96
 
1.9%
t95
 
1.9%
Other values (4)273
 
5.4%

clock_speed
Real number (ℝ≥0)

speed at which microprocessor executes instructions

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.52225
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:36.793327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.8160042089
Coefficient of variation (CV)0.5360513772
Kurtosis-1.323417222
Mean1.52225
Median Absolute Deviation (MAD)0.8
Skewness0.1780841203
Sum3044.5
Variance0.6658628689
MonotocityNot monotonic
2021-05-02T12:20:36.920613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5413
20.6%
2.885
 
4.2%
2.378
 
3.9%
1.676
 
3.8%
2.176
 
3.8%
2.574
 
3.7%
0.674
 
3.7%
1.470
 
3.5%
1.368
 
3.4%
267
 
3.4%
Other values (16)919
46.0%
ValueCountFrequency (%)
0.5413
20.6%
0.674
 
3.7%
0.764
 
3.2%
0.858
 
2.9%
0.958
 
2.9%
161
 
3.0%
1.151
 
2.5%
1.256
 
2.8%
1.368
 
3.4%
1.470
 
3.5%
ValueCountFrequency (%)
328
 
1.4%
2.962
3.1%
2.885
4.2%
2.755
2.8%
2.655
2.8%
2.574
3.7%
2.458
2.9%
2.378
3.9%
2.259
2.9%
2.176
3.8%

dual_sim
Categorical

Has dual sim support or not

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size116.4 KiB
yes
645 
no
607 
No
171 
Yes
150 
NO
106 
Other values (5)
321 

Length

Max length3
Median length3
Mean length2.558
Min length2

Characters and Unicode

Total characters5116
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowYES
3rd rowYes
4th rowno
5th rowNO
ValueCountFrequency (%)
yes645
32.2%
no607
30.3%
No171
 
8.6%
Yes150
 
7.5%
NO106
 
5.3%
YES99
 
5.0%
has68
 
3.4%
Has57
 
2.9%
Not51
 
2.5%
not46
 
2.3%
2021-05-02T12:20:37.170926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:37.259632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
yes894
44.7%
no884
44.2%
has125
 
6.2%
not97
 
4.9%

Most occurring characters

ValueCountFrequency (%)
s920
18.0%
o875
17.1%
e795
15.5%
n653
12.8%
y645
12.6%
N328
 
6.4%
Y249
 
4.9%
a125
 
2.4%
O106
 
2.1%
E99
 
1.9%
Other values (4)321
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4178
81.7%
Uppercase Letter938
 
18.3%

Most frequent character per category

ValueCountFrequency (%)
s920
22.0%
o875
20.9%
e795
19.0%
n653
15.6%
y645
15.4%
a125
 
3.0%
t97
 
2.3%
h68
 
1.6%
ValueCountFrequency (%)
N328
35.0%
Y249
26.5%
O106
 
11.3%
E99
 
10.6%
S99
 
10.6%
H57
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin5116
100.0%

Most frequent character per script

ValueCountFrequency (%)
s920
18.0%
o875
17.1%
e795
15.5%
n653
12.8%
y645
12.6%
N328
 
6.4%
Y249
 
4.9%
a125
 
2.4%
O106
 
2.1%
E99
 
1.9%
Other values (4)321
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5116
100.0%

Most frequent character per block

ValueCountFrequency (%)
s920
18.0%
o875
17.1%
e795
15.5%
n653
12.8%
y645
12.6%
N328
 
6.4%
Y249
 
4.9%
a125
 
2.4%
O106
 
2.1%
E99
 
1.9%
Other values (4)321
 
6.3%

fc
Real number (ℝ≥0)

ZEROS

Front Camera mega pixels

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3095
Minimum0
Maximum19
Zeros474
Zeros (%)23.7%
Memory size15.8 KiB
2021-05-02T12:20:37.429837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.341443748
Coefficient of variation (CV)1.007412402
Kurtosis0.2770763246
Mean4.3095
Median Absolute Deviation (MAD)3
Skewness1.019811411
Sum8619
Variance18.84813382
MonotocityNot monotonic
2021-05-02T12:20:37.820851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
5139
 
7.0%
4133
 
6.7%
6112
 
5.6%
7100
 
5.0%
978
 
3.9%
877
 
3.9%
Other values (10)283
14.1%
ValueCountFrequency (%)
0474
23.7%
1245
12.2%
2189
 
9.4%
3170
 
8.5%
4133
 
6.7%
5139
 
7.0%
6112
 
5.6%
7100
 
5.0%
877
 
3.9%
978
 
3.9%
ValueCountFrequency (%)
191
 
0.1%
1811
 
0.5%
176
 
0.3%
1624
 
1.2%
1523
 
1.1%
1420
 
1.0%
1340
2.0%
1245
2.2%
1151
2.5%
1062
3.1%

four_g
Categorical

Has 4G or not. 1 = yes , 0 = no

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
1
1043 
0
957 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
11043
52.1%
0957
47.9%
2021-05-02T12:20:38.048084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:38.126734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring characters

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ValueCountFrequency (%)
11043
52.1%
0957
47.9%

int_memory
Real number (ℝ≥0)

internal Memory in Gigabytes

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0465
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:38.217084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.14571496
Coefficient of variation (CV)0.5662307882
Kurtosis-1.21607403
Mean32.0465
Median Absolute Deviation (MAD)16
Skewness0.05788932785
Sum64093
Variance329.2669712
MonotocityNot monotonic
2021-05-02T12:20:38.352707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2747
 
2.4%
1445
 
2.2%
1645
 
2.2%
242
 
2.1%
5742
 
2.1%
740
 
2.0%
4240
 
2.0%
4439
 
1.9%
3039
 
1.9%
637
 
1.8%
Other values (53)1584
79.2%
ValueCountFrequency (%)
242
2.1%
325
1.2%
420
1.0%
536
1.8%
637
1.8%
740
2.0%
837
1.8%
935
1.8%
1036
1.8%
1134
1.7%
ValueCountFrequency (%)
6431
1.6%
6330
1.5%
6221
1.1%
6127
1.4%
6027
1.4%
5918
0.9%
5836
1.8%
5742
2.1%
5627
1.4%
5529
1.5%

m_dep
Real number (ℝ≥0)

Mobile Depth in cm

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:38.480708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2884155496
Coefficient of variation (CV)0.5748192319
Kurtosis-1.274348884
Mean0.50175
Median Absolute Deviation (MAD)0.3
Skewness0.08908200979
Sum1003.5
Variance0.08318352926
MonotocityNot monotonic
2021-05-02T12:20:38.579713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.8208
10.4%
0.5205
10.2%
0.7200
10.0%
0.3199
10.0%
0.9195
9.8%
0.6186
9.3%
0.4168
8.4%
1106
 
5.3%
ValueCountFrequency (%)
0.1320
16.0%
0.2213
10.7%
0.3199
10.0%
0.4168
8.4%
0.5205
10.2%
0.6186
9.3%
0.7200
10.0%
0.8208
10.4%
0.9195
9.8%
1106
 
5.3%
ValueCountFrequency (%)
1106
 
5.3%
0.9195
9.8%
0.8208
10.4%
0.7200
10.0%
0.6186
9.3%
0.5205
10.2%
0.4168
8.4%
0.3199
10.0%
0.2213
10.7%
0.1320
16.0%

mobile_wt
Real number (ℝ≥0)

Weight of mobile phone

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.249
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:38.704730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.3996549
Coefficient of variation (CV)0.2524057562
Kurtosis-1.210376474
Mean140.249
Median Absolute Deviation (MAD)31
Skewness0.006558157429
Sum280498
Variance1253.135567
MonotocityNot monotonic
2021-05-02T12:20:38.845428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18228
 
1.4%
18527
 
1.4%
10127
 
1.4%
14626
 
1.3%
19926
 
1.3%
8825
 
1.2%
10525
 
1.2%
19825
 
1.2%
8924
 
1.2%
14523
 
1.1%
Other values (111)1744
87.2%
ValueCountFrequency (%)
8021
1.1%
8113
0.7%
8215
0.8%
8319
0.9%
8417
0.9%
8513
0.7%
8619
0.9%
8715
0.8%
8825
1.2%
8924
1.2%
ValueCountFrequency (%)
20019
0.9%
19926
1.3%
19825
1.2%
19719
0.9%
19620
1.0%
19511
0.5%
19416
0.8%
19315
0.8%
19215
0.8%
19115
0.8%

n_cores
Real number (ℝ≥0)

Number of cores of processor

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5205
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:38.968066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.287836718
Coefficient of variation (CV)0.5061025811
Kurtosis-1.229749767
Mean4.5205
Median Absolute Deviation (MAD)2
Skewness0.003627508314
Sum9041
Variance5.234196848
MonotocityNot monotonic
2021-05-02T12:20:39.055511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4274
13.7%
7259
13.0%
8256
12.8%
2247
12.3%
5246
12.3%
3246
12.3%
1242
12.1%
6230
11.5%
ValueCountFrequency (%)
1242
12.1%
2247
12.3%
3246
12.3%
4274
13.7%
5246
12.3%
6230
11.5%
7259
13.0%
8256
12.8%
ValueCountFrequency (%)
8256
12.8%
7259
13.0%
6230
11.5%
5246
12.3%
4274
13.7%
3246
12.3%
2247
12.3%
1242
12.1%

pc
Real number (ℝ≥0)

ZEROS

Primary Camera mega pixels

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9165
Minimum0
Maximum20
Zeros101
Zeros (%)5.1%
Memory size15.8 KiB
2021-05-02T12:20:39.169829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.064314941
Coefficient of variation (CV)0.6115378351
Kurtosis-1.171498795
Mean9.9165
Median Absolute Deviation (MAD)5
Skewness0.01730615047
Sum19833
Variance36.77591571
MonotocityNot monotonic
2021-05-02T12:20:39.278184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10122
 
6.1%
7119
 
5.9%
9112
 
5.6%
20110
 
5.5%
14104
 
5.2%
1104
 
5.2%
0101
 
5.1%
299
 
5.0%
1799
 
5.0%
695
 
4.8%
Other values (11)935
46.8%
ValueCountFrequency (%)
0101
5.1%
1104
5.2%
299
5.0%
393
4.7%
495
4.8%
559
2.9%
695
4.8%
7119
5.9%
889
4.5%
9112
5.6%
ValueCountFrequency (%)
20110
5.5%
1983
4.2%
1882
4.1%
1799
5.0%
1688
4.4%
1592
4.6%
14104
5.2%
1385
4.2%
1290
4.5%
1179
4.0%

px_height
Real number (ℝ≥0)

Pixel Resolution Height

Distinct1137
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.108
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Memory size15.8 KiB
2021-05-02T12:20:39.414305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.95
Q1282.75
median564
Q3947.25
95-th percentile1485.05
Maximum1960
Range1960
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation443.7808108
Coefficient of variation (CV)0.6879170787
Kurtosis-0.3158654936
Mean645.108
Median Absolute Deviation (MAD)318
Skewness0.6662712561
Sum1290216
Variance196941.408
MonotocityNot monotonic
2021-05-02T12:20:39.544421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477
 
0.4%
1796
 
0.3%
3716
 
0.3%
2756
 
0.3%
5265
 
0.2%
3275
 
0.2%
6745
 
0.2%
6675
 
0.2%
3565
 
0.2%
565
 
0.2%
Other values (1127)1945
97.2%
ValueCountFrequency (%)
02
0.1%
11
 
0.1%
21
 
0.1%
32
0.1%
43
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
82
0.1%
91
 
0.1%
ValueCountFrequency (%)
19601
0.1%
19491
0.1%
19201
0.1%
19141
0.1%
19011
0.1%
18991
0.1%
18951
0.1%
18781
0.1%
18741
0.1%
18691
0.1%

px_width
Real number (ℝ≥0)

Pixel Resolution Width

Distinct1109
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:39.680562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile579.85
Q1874.75
median1247
Q31633
95-th percentile1929.05
Maximum1998
Range1498
Interquartile range (IQR)758.25

Descriptive statistics

Standard deviation432.1994469
Coefficient of variation (CV)0.3453408663
Kurtosis-1.186005229
Mean1251.5155
Median Absolute Deviation (MAD)376
Skewness0.01478747377
Sum2503031
Variance186796.3619
MonotocityNot monotonic
2021-05-02T12:20:39.812500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8747
 
0.4%
12477
 
0.4%
13836
 
0.3%
14696
 
0.3%
14636
 
0.3%
14295
 
0.2%
17265
 
0.2%
19235
 
0.2%
12345
 
0.2%
12635
 
0.2%
Other values (1099)1943
97.2%
ValueCountFrequency (%)
5002
0.1%
5012
0.1%
5031
 
0.1%
5061
 
0.1%
5074
0.2%
5081
 
0.1%
5092
0.1%
5103
0.1%
5112
0.1%
5122
0.1%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19961
 
0.1%
19953
0.1%
19942
 
0.1%
19921
 
0.1%
19911
 
0.1%
19901
 
0.1%
19893
0.1%
19885
0.2%

ram
Real number (ℝ≥0)

Random Access Memory in Mega Bytes

Distinct1562
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.213
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:39.959095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207.5
median2146.5
Q33064.5
95-th percentile3826.35
Maximum3998
Range3742
Interquartile range (IQR)1857

Descriptive statistics

Standard deviation1084.732044
Coefficient of variation (CV)0.5106512594
Kurtosis-1.19191307
Mean2124.213
Median Absolute Deviation (MAD)932.5
Skewness0.006628035399
Sum4248426
Variance1176643.606
MonotocityNot monotonic
2021-05-02T12:20:40.091131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26104
 
0.2%
22274
 
0.2%
31424
 
0.2%
14644
 
0.2%
12294
 
0.2%
3153
 
0.1%
19583
 
0.1%
12773
 
0.1%
17243
 
0.1%
37033
 
0.1%
Other values (1552)1965
98.2%
ValueCountFrequency (%)
2561
0.1%
2582
0.1%
2591
0.1%
2621
0.1%
2631
0.1%
2651
0.1%
2671
0.1%
2731
0.1%
2771
0.1%
2782
0.1%
ValueCountFrequency (%)
39981
0.1%
39961
0.1%
39931
0.1%
39912
0.1%
39901
0.1%
39841
0.1%
39781
0.1%
39711
0.1%
39702
0.1%
39691
0.1%

sc_h
Real number (ℝ≥0)

Screen Height of mobile in cm

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3065
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:40.220812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.213245004
Coefficient of variation (CV)0.3423593227
Kurtosis-1.190791247
Mean12.3065
Median Absolute Deviation (MAD)4
Skewness-0.09888424098
Sum24613
Variance17.75143347
MonotocityNot monotonic
2021-05-02T12:20:40.325353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17193
 
9.7%
12157
 
7.8%
7151
 
7.5%
16143
 
7.1%
14143
 
7.1%
15135
 
6.8%
13131
 
6.6%
11126
 
6.3%
10125
 
6.2%
19124
 
6.2%
Other values (5)572
28.6%
ValueCountFrequency (%)
597
4.9%
6114
5.7%
7151
7.5%
8117
5.9%
9124
6.2%
10125
6.2%
11126
6.3%
12157
7.8%
13131
6.6%
14143
7.1%
ValueCountFrequency (%)
19124
6.2%
18120
6.0%
17193
9.7%
16143
7.1%
15135
6.8%
14143
7.1%
13131
6.6%
12157
7.8%
11126
6.3%
10125
6.2%

sc_w
Real number (ℝ≥0)

ZEROS

Screen Width of mobile in cm

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.767
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Memory size15.8 KiB
2021-05-02T12:20:40.444147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.356397606
Coefficient of variation (CV)0.7554010067
Kurtosis-0.3895227894
Mean5.767
Median Absolute Deviation (MAD)3
Skewness0.6337870734
Sum11534
Variance18.9782001
MonotocityNot monotonic
2021-05-02T12:20:40.553192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1210
10.5%
3199
10.0%
4182
9.1%
0180
9.0%
5161
 
8.1%
2156
 
7.8%
7132
 
6.6%
6130
 
6.5%
8125
 
6.2%
10107
 
5.3%
Other values (9)418
20.9%
ValueCountFrequency (%)
0180
9.0%
1210
10.5%
2156
7.8%
3199
10.0%
4182
9.1%
5161
8.1%
6130
6.5%
7132
6.6%
8125
6.2%
997
4.9%
ValueCountFrequency (%)
188
 
0.4%
1719
 
0.9%
1629
 
1.5%
1531
 
1.6%
1433
 
1.7%
1349
2.5%
1268
3.4%
1184
4.2%
10107
5.3%
997
4.9%

talk_time
Real number (ℝ≥0)

longest time that a single battery charge will last when you are

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.011
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Memory size15.8 KiB
2021-05-02T12:20:40.674603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.463955198
Coefficient of variation (CV)0.4962269728
Kurtosis-1.218590963
Mean11.011
Median Absolute Deviation (MAD)5
Skewness0.009511762222
Sum22022
Variance29.8548064
MonotocityNot monotonic
2021-05-02T12:20:40.779765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7124
 
6.2%
4123
 
6.2%
16116
 
5.8%
15115
 
5.8%
19113
 
5.7%
6111
 
5.5%
10105
 
5.2%
8104
 
5.2%
11103
 
5.1%
20102
 
5.1%
Other values (9)884
44.2%
ValueCountFrequency (%)
299
5.0%
394
4.7%
4123
6.2%
593
4.7%
6111
5.5%
7124
6.2%
8104
5.2%
9100
5.0%
10105
5.2%
11103
5.1%
ValueCountFrequency (%)
20102
5.1%
19113
5.7%
18100
5.0%
1798
4.9%
16116
5.8%
15115
5.8%
14101
5.1%
13100
5.0%
1299
5.0%
11103
5.1%

three_g
Categorical

Has 3G or not

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size116.9 KiB
yes
945 
Yes
304 
no
301 
YES
140 
No
 
86
Other values (5)
224 

Length

Max length3
Median length3
Mean length2.7835
Min length2

Characters and Unicode

Total characters5567
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowYes
5th rowYes
ValueCountFrequency (%)
yes945
47.2%
Yes304
 
15.2%
no301
 
15.0%
YES140
 
7.0%
No86
 
4.3%
Has68
 
3.4%
has66
 
3.3%
NO46
 
2.3%
Not23
 
1.1%
not21
 
1.1%
2021-05-02T12:20:41.026111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:41.114032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
yes1389
69.5%
no433
 
21.6%
has134
 
6.7%
not44
 
2.2%

Most occurring characters

ValueCountFrequency (%)
s1383
24.8%
e1249
22.4%
y945
17.0%
Y444
 
8.0%
o431
 
7.7%
n322
 
5.8%
N155
 
2.8%
E140
 
2.5%
S140
 
2.5%
a134
 
2.4%
Other values (4)224
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4574
82.2%
Uppercase Letter993
 
17.8%

Most frequent character per category

ValueCountFrequency (%)
s1383
30.2%
e1249
27.3%
y945
20.7%
o431
 
9.4%
n322
 
7.0%
a134
 
2.9%
h66
 
1.4%
t44
 
1.0%
ValueCountFrequency (%)
Y444
44.7%
N155
 
15.6%
E140
 
14.1%
S140
 
14.1%
H68
 
6.8%
O46
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin5567
100.0%

Most frequent character per script

ValueCountFrequency (%)
s1383
24.8%
e1249
22.4%
y945
17.0%
Y444
 
8.0%
o431
 
7.7%
n322
 
5.8%
N155
 
2.8%
E140
 
2.5%
S140
 
2.5%
a134
 
2.4%
Other values (4)224
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5567
100.0%

Most frequent character per block

ValueCountFrequency (%)
s1383
24.8%
e1249
22.4%
y945
17.0%
Y444
 
8.0%
o431
 
7.7%
n322
 
5.8%
N155
 
2.8%
E140
 
2.5%
S140
 
2.5%
a134
 
2.4%
Other values (4)224
 
4.0%

touch_screen
Categorical

Has touch screen or not, 1 = yes, 0 = no

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
1
1006 
0
994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
11006
50.3%
0994
49.7%
2021-05-02T12:20:41.409433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:41.482321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring characters

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ValueCountFrequency (%)
11006
50.3%
0994
49.7%

wifi
Categorical

Has wifi or not

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size116.4 KiB
no
645 
yes
619 
Yes
205 
No
181 
YES
97 
Other values (5)
253 

Length

Max length3
Median length3
Mean length2.5465
Min length2

Characters and Unicode

Total characters5093
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no645
32.2%
yes619
30.9%
Yes205
 
10.2%
No181
 
9.0%
YES97
 
4.9%
NO81
 
4.0%
Has55
 
2.8%
not40
 
2.0%
Not39
 
1.9%
has38
 
1.9%
2021-05-02T12:20:41.676898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:41.759045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
yes921
46.1%
no907
45.4%
has93
 
4.7%
not79
 
4.0%

Most occurring characters

ValueCountFrequency (%)
s917
18.0%
o905
17.8%
e824
16.2%
n685
13.4%
y619
12.2%
Y302
 
5.9%
N301
 
5.9%
E97
 
1.9%
S97
 
1.9%
a93
 
1.8%
Other values (4)253
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4160
81.7%
Uppercase Letter933
 
18.3%

Most frequent character per category

ValueCountFrequency (%)
s917
22.0%
o905
21.8%
e824
19.8%
n685
16.5%
y619
14.9%
a93
 
2.2%
t79
 
1.9%
h38
 
0.9%
ValueCountFrequency (%)
Y302
32.4%
N301
32.3%
E97
 
10.4%
S97
 
10.4%
O81
 
8.7%
H55
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin5093
100.0%

Most frequent character per script

ValueCountFrequency (%)
s917
18.0%
o905
17.8%
e824
16.2%
n685
13.4%
y619
12.2%
Y302
 
5.9%
N301
 
5.9%
E97
 
1.9%
S97
 
1.9%
a93
 
1.8%
Other values (4)253
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5093
100.0%

Most frequent character per block

ValueCountFrequency (%)
s917
18.0%
o905
17.8%
e824
16.2%
n685
13.4%
y619
12.2%
Y302
 
5.9%
N301
 
5.9%
E97
 
1.9%
S97
 
1.9%
a93
 
1.8%
Other values (4)253
 
5.0%

price_category
Categorical

This is the target variable with indicating if the mobile phone got a high price. 1 = yes, 0 = no

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
1500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01500
75.0%
1500
 
25.0%
2021-05-02T12:20:42.044782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-02T12:20:42.122371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
01500
75.0%
1500
 
25.0%

Most occurring characters

ValueCountFrequency (%)
01500
75.0%
1500
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
100.0%

Most frequent character per category

ValueCountFrequency (%)
01500
75.0%
1500
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common2000
100.0%

Most frequent character per script

ValueCountFrequency (%)
01500
75.0%
1500
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2000
100.0%

Most frequent character per block

ValueCountFrequency (%)
01500
75.0%
1500
 
25.0%

Interactions

2021-05-02T12:20:02.941530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:03.127137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:03.270461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:03.428623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:03.575747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:03.716266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:03.863997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:04.013973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:04.170625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:04.425582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:04.591028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:04.760698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:04.909867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:05.066862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:05.250532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:05.414029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:05.555678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:05.714371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:05.869270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.006551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.169636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.341475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.491112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.638769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.780507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:06.959901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:07.117393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:07.300382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:07.464853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:07.615204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:07.808206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:07.949156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:08.085990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:08.262014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:08.386003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:08.507272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:08.639598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:08.819503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:09.072184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:09.210581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:09.339725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:09.525634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:09.707571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:09.889021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:10.072042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:10.239586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:10.441255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:10.582326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:10.760119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:10.902728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:11.042257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:11.297313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:11.475188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:11.611921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:11.738947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:11.877867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.017036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.166312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.315376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.454658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.595840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.721900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.857615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:12.993609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.135147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.276831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.412533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.549042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.680799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.817394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:13.954772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.087073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.220136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.346143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.611372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.734726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.858975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:14.987814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.118903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.251137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.376211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.500777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.623688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.764215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:15.906084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.056246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.212864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.340871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.471541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.603742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.740870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:16.886219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.032308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.175696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.315058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.449210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.580616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.716696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.852473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:17.997189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.140969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.276378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.416187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.554897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.683210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.815641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:18.961205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.110989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.254185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.397110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.535490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.671987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.811262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:19.981229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:20.149164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:20.308022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:20.460934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:20.605341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:20.916721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.053250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.202452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.353112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.505155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.661130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.806617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:21.959249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.108911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.258592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.409209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.548776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.682871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.824764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:22.957441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.098242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.249272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.395916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.537556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.675905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.828002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:23.985163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.142329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.289745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.433224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.572514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.715476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.852815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:24.984865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:25.159400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:25.341526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:25.589052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:25.739585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:25.968458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:26.155215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:26.332729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:26.487418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:26.637374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:26.774103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:26.909258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:27.057693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:27.250835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:27.440022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:27.583496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:27.741398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:27.900707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.058542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.217191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.365903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.507023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.641561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.773585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:28.911586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:29.341229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:29.495129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:29.642348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:29.771680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:29.902613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.030423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.176744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.321314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.460530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.599179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.744673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:30.875406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.014731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.152212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.288159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.437143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.577029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.707958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.849795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:31.989116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.134996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.281813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.422681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.565664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.703440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.847676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:32.995833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.140629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.277992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.425198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.570308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.705006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.846302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:33.992468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:34.139625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:34.292826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:34.443041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:34.594908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-02T12:20:34.736156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-02T12:20:42.218910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-02T12:20:42.497756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-02T12:20:42.776455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-02T12:20:43.065784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-02T12:20:43.340852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-02T12:20:35.023965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-02T12:20:35.545472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idbattery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_category
00842no2.2no1070.6188222075625499719no0yes0
111021yes0.5YES01530.713636905198826311737yes1no0
22563yes0.5Yes21410.9145561263171626031129yes1no0
33615has2.5no00100.81316912161786276916811Yes0no0
441821yes1.2NO131440.61412141208121214118215Yes1no0
551859No0.5yes30220.71641710041654106717110Has0no0
661821no1.7NO41100.81398103811018322013818yes0Yes1
771954no0.5Yes00240.81874051211497001635yes1yes0
881445yes0.5NO00530.7174714386836109917120yes0not0
99509yes0.6has2190.19351511371224513191012yes0no0

Last rows

idbattery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_category
199019901617yes2.4no81360.885197431426296537YES0No0
199119911882no2.0no111440.81138194743357919820yes1no1
19921992674has2.9yes10210.21983457618091180634yes1yes0
199319931467yes0.5no00180.6122508881099396215115yes1Yes1
19941994858no2.2no10500.184125281416397817163yes1no1
19951995794yes0.5yes0120.81066141222189066813419yes1No0
199619961965yes2.6yes00390.21874391519652032111016has1yes0
199719971911no0.9yes11360.71088386816323057915yes1no1
199819981512no0.9not41460.114555336670869181019Yes1yes0
19991999510Yes2.0yes51450.916861648375439191942yes1yes1